| --- |
| base_model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit |
| library_name: peft |
| model_name: math-lora-adapter |
| tags: |
| - base_model:adapter:unsloth/Llama-3.2-1B-Instruct-bnb-4bit |
| - lora |
| - sft |
| - transformers |
| - trl |
| licence: license |
| pipeline_tag: text-generation |
| --- |
| |
| # Model Card for math-lora-adapter |
|
|
| This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-bnb-4bit). |
| It has been trained using [TRL](https://github.com/huggingface/trl). |
|
|
| ## Quick start |
|
|
| ```python |
| from transformers import pipeline |
| |
| question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" |
| generator = pipeline("text-generation", model="None", device="cuda") |
| output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] |
| print(output["generated_text"]) |
| ``` |
|
|
| ## Training procedure |
|
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| |
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| This model was trained with SFT. |
|
|
| ### Framework versions |
|
|
| - PEFT 0.18.1 |
| - TRL: 0.29.0 |
| - Transformers: 4.57.6 |
| - Pytorch: 2.9.1 |
| - Datasets: 4.6.1 |
| - Tokenizers: 0.22.2 |
|
|
| ## Citations |
|
|
|
|
|
|
| Cite TRL as: |
| |
| ```bibtex |
| @software{vonwerra2020trl, |
| title = {{TRL: Transformers Reinforcement Learning}}, |
| author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, |
| license = {Apache-2.0}, |
| url = {https://github.com/huggingface/trl}, |
| year = {2020} |
| } |
| ``` |
